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Automatic depression discrimination on FNIRS by using general linear model and SVM

机译:使用一般线性模型和SVM对FNIR上的自动抑郁症

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A method is proposed to distinguish patients with depression from healthy persons using data measured by Functional Near Infrared Spectroscopy (FNIRS) during a cognitive task. Firstly, General Linear Model (GLM) is used to extract features from 52-channel FNIRS data of patients with depression and normal healthy persons. Then a Support Vector Machine (SVM) classifier is designed for classification. The results of experiment show that the method can achieve a satisfactory classification with the accuracy 89.71% for total and 92.59% for patients. Also, the results suggest that FNIRS is a promising clinical technique in the diagnosis and therapy of depression.
机译:提出一种方法,以利用通过在认知任务期间使用功能近红外光谱(Fnirs)测量的数据来区分患者从健康人的抑郁症。首先,通用线性模型(GLM)用于从抑郁和正常健康人员的患者的52通道FNIRS数据中提取特征。然后设计支持向量机(SVM)分类器用于分类。实验结果表明,该方法可实现令人满意的分类,精度为89.71%,患者为92.59%。此外,结果表明Fnirs是抑郁症诊断和治疗的有前途的临床技术。

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